Performance of TyrerCuzick Model for Breast Cancer Risk Assessment among Pakistan’s Females

Background: Breast cancer incidence is highest in Pakistan among Asian countries. The known risk factors are family history, hormonal exposure, benign proliferative diseases, and high mammographic density which are included in the TyrerCuzick model. The model needs validation studies to implement in prediction, screening, and prevention strategies among different populations. This study aims to validate the TyrerCuzick model for Pakistan's females. Methods and Materials: A total of 317 biopsy-proven breast cancer patients from the breast surgery clinic at Liaquat National Hospital were included. The 10 years risk score is calculated by applying the TyrerCuzick model software. Subcategories of low risk <2%, moderate risk 2-7%, and high risk >8% were identified. Further risk group stratification is done to find the association with individual factors i.e., age group, menopausal status, family history, and mammographic density. Results: The mean TyrerCuzick score was low to moderate i.e. 2.23±1.66. The score was distributed as low risk 174(54.9%), moderate risk 137(43.2%), and high risk 6(1.9%). Low risk was observed among 116(81.7%) of less than 50 years old, 105(78.9%) premenopausal, 113(59.8%) with no family history, and 120 patients (59.7%) with low mammographic density. Most of the moderate risk was found in 113(64.6%) of more than 50 years old, 109(60.2%) with postmenopausal, 24(61.5%) with family history, 58(50%) with high mammographic density respectively. Conclusion: The TyrerCuzick model can predict risk for developing breast cancer among Pakistan’s femalesclose to accurate among older age, postmenopausal, family history of breast cancer, and high mammographic density.

Biological Mechanisms and Therapeutic Opportunities in Mammographic Density and Breast Cancer Risk

Mammographic density is an important risk factor for breast cancer; women with extremely dense breasts have a four to six fold increased risk of breast cancer compared to women with mostly fatty breasts, when matched with age and body mass index. High mammographic density is characterised by high proportions of stroma, containing fibroblasts, collagen and immune cells that suggest a pro-tumour inflammatory microenvironment. However, the biological mechanisms that drive increased mammographic density and the associated increased risk of breast cancer are not yet understood. Inflammatory factors such as monocyte chemotactic protein 1, peroxidase enzymes, transforming growth factor beta, and tumour necrosis factor alpha have been implicated in breast development as well as breast cancer risk, and also influence functions of stromal fibroblasts. Here, the current knowledge and understanding of the underlying biological mechanisms that lead to high mammographic density and the associated increased risk of breast cancer are reviewed, with particular consideration to potential immune factors that may contribute to this process.

Mechanical Pressure Driving Proteoglycan Expression in Mammographic Density: a Self-perpetuating Cycle?

Regions of high mammographic density (MD) in the breast are characterised by a proteoglycan (PG)-rich fibrous stroma, where PGs mediate aligned collagen fibrils to control tissue stiffness and hence the response to mechanical forces. Literature is accumulating to support the notion that mechanical stiffness may drive PG synthesis in the breast contributing to MD. We review emerging patterns in MD and other biological settings, of a positive feedback cycle of force promoting PG synthesis, such as in articular cartilage, due to increased pressure on weight bearing joints. Furthermore, we present evidence to suggest a pro-tumorigenic effect of increased mechanical force on epithelial cells in contexts where PG-mediated, aligned collagen fibrous tissue abounds, with implications for breast cancer development attributable to high MD. Finally, we summarise means through which this positive feedback mechanism of PG synthesis may be intercepted to reduce mechanical force within tissues and thus reduce disease burden.

Pten regulates collagen fibrillogenesis by fibroblasts through SPARC

Collagen deposition contributes to both high mammographic density and breast cancer progression. Low stromal PTEN expression has been observed in as many as half of breast tumors and is associated with increases in collagen deposition, however the mechanism connecting PTEN loss to increased collagen deposition remains unclear. Here, we demonstrate that Pten knockout in fibroblasts using an Fsp-Cre;PtenloxP/loxP mouse model increases collagen fiber number and fiber size within the mammary gland. Pten knockout additionally upregulated Sparc transcription in fibroblasts and promoted collagen shuttling out of the cell. Interestingly, SPARC mRNA expression was observed to be significantly elevated in the tumor stroma as compared to the normal breast in several patient cohorts. While SPARC knockdown via shRNA did not affect collagen shuttling, it notably decreased assembly of exogenous collagen. In addition, SPARC knockdown decreased fibronectin assembly and alignment of the extracellular matrix in an in vitro fibroblast-derived matrix model. Overall, these data indicate upregulation of SPARC is a mechanism by which PTEN regulates collagen deposition in the mammary gland stroma.

Does mammographic density remain a radiological challenge in the digital era?

Background The low subject contrast between cancerous and fibroglandular tissue could obscure breast abnormalities. Purpose To investigate radiologists’ performance for detection of breast cancer in low and high mammographic density (MD) when cases are digitally acquired. Material and Methods A test set of 60 digital mammography cases, of which 20 were cancerous, were examined by 17 radiologists. Mammograms were categorized as low (≤50%) or high (>50%) MD and rated for suspicion of malignancy using the Royal Australian and New Zealand College of Radiology (RANZCR) classification system. Radiologist demographics including cases read per year, age, subspecialty, and years of reporting were recorded. Radiologist performance was analyzed by the following metrics: sensitivity; specificity; area under the receiver operating characteristic (ROC) curve (AUC), location sensitivity, and jackknife free-response ROC (JAFROC) figure of merit (FOM). Results Comparing high to low MD cases, radiologists showed a significantly higher sensitivity ( P = 0.015), AUC ( P = 0.003), location sensitivity ( P = 0.002), and JAFROC FOM ( P = 0.001). In high compared to low MD cases, radiologists with <1000 annual reads and radiologists with no mammographic subspecialty had significantly higher AUC, location sensitivity, and JAFROC FOM. Radiologists with ≥1000 annual reads and radiologists with mammography subspecialty demonstrated a significant increase in location sensitivity in high compared to low MD cases. Conclusion In this experimental situation, radiologists’ performance was higher when reading cases with high compared to low MD. Experienced radiologists were able to precisely localize lesions in breasts with higher MD. Further studies in unselected screening materials are needed to verify the results.

YY1 is a cis-regulator in the organoid models of high mammographic density

Motivation Our previous study has shown that ERBB2 is overexpressed in the organoid model of MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density (MD). We now aim to identify key transcription factors (TFs) and functional enhancers that regulate processes associated with increased stiffness of the microenvironment in the organoid models of premalignant human mammary cell lines. Results 3D colony organizations and the cis-regulatory networks of two human mammary epithelial cell lines (184A1 and MCF10A) are investigated as a function of the increased stiffness of the microenvironment within the range of MD. The 3D colonies are imaged using confocal microscopy, and the morphometries of colony organizations and heterogeneity are quantified as a function of the stiffness of the microenvironment using BioSig3D. In a surrogate assay, colony organizations are profiled by transcriptomics. Transcriptome data are enriched by correlative analysis with the computed morphometric indices. Next, a subset of enriched data are processed against publicly available ChIP-Seq data using Model-based Analysis of Regulation of Gene Expression to predict regulatory transcription factors. This integrative analysis of morphometric and transcriptomic data predicted YY1 as one of the cis-regulators in both cell lines as a result of the increased stiffness of the microenvironment. Subsequent experiments validated that YY1 is expressed at protein and mRNA levels for MCF10A and 184A1, respectively. Also, there is a causal relationship between activation of YY1 and ERBB2 when YY1 is overexpressed at the protein level in MCF10A. Supplementary information Supplementary data are available at Bioinformatics online.